Point Cloud Library (PCL) 1.12.0
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vector_average.h
1/*
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37
38#pragma once
39
40#include <Eigen/Core> // for Matrix
41
42#include <pcl/memory.h>
43#include <pcl/pcl_macros.h>
44
45namespace pcl
46{
47 /** \brief Calculates the weighted average and the covariance matrix
48 *
49 * A class to calculate the weighted average and the covariance matrix of a set of vectors with given weights.
50 * The original data is not saved. Mean and covariance are calculated iteratively.
51 * \author Bastian Steder
52 * \ingroup common
53 */
54 template <typename real, int dimension>
56 {
57 public:
58 using VectorType = Eigen::Matrix<real, dimension, 1>;
59 using MatrixType = Eigen::Matrix<real, dimension, dimension>;
60 //-----CONSTRUCTOR&DESTRUCTOR-----
61 /** Constructor - dimension gives the size of the vectors to work with. */
63
64 //-----METHODS-----
65 /** Reset the object to work with a new data set */
66 inline void
67 reset ();
68
69 /** Get the mean of the added vectors */
70 inline const
71 VectorType& getMean () const { return mean_;}
72
73 /** Get the covariance matrix of the added vectors */
74 inline const
76
77 /** Get the summed up weight of all added vectors */
78 inline real
80
81 /** Get the number of added vectors */
82 inline unsigned int
84
85 /** Add a new sample */
86 inline void
87 add (const VectorType& sample, real weight=1.0);
88
89 /** Do Principal component analysis */
90 inline void
91 doPCA (VectorType& eigen_values, VectorType& eigen_vector1,
92 VectorType& eigen_vector2, VectorType& eigen_vector3) const;
93
94 /** Do Principal component analysis */
95 inline void
96 doPCA (VectorType& eigen_values) const;
97
98 /** Get the eigenvector corresponding to the smallest eigenvalue */
99 inline void
100 getEigenVector1 (VectorType& eigen_vector1) const;
101
103
104 //-----VARIABLES-----
105
106
107 protected:
108 //-----METHODS-----
109 //-----VARIABLES-----
110 unsigned int noOfSamples_ = 0;
112 VectorType mean_ = VectorType::Identity ();
113 MatrixType covariance_ = MatrixType::Identity ();
114 };
115
119} // END namespace
120
121#include <pcl/common/impl/vector_average.hpp>
Calculates the weighted average and the covariance matrix.
void add(const VectorType &sample, real weight=1.0)
Add a new sample.
void reset()
Reset the object to work with a new data set.
VectorAverage()
Constructor - dimension gives the size of the vectors to work with.
Eigen::Matrix< real, dimension, 1 > VectorType
void doPCA(VectorType &eigen_values, VectorType &eigen_vector1, VectorType &eigen_vector2, VectorType &eigen_vector3) const
Do Principal component analysis.
Eigen::Matrix< real, dimension, dimension > MatrixType
real getAccumulatedWeight() const
Get the summed up weight of all added vectors.
const VectorType & getMean() const
Get the mean of the added vectors.
void getEigenVector1(VectorType &eigen_vector1) const
Get the eigenvector corresponding to the smallest eigenvalue.
unsigned int getNoOfSamples()
Get the number of added vectors.
unsigned int noOfSamples_
const MatrixType & getCovariance() const
Get the covariance matrix of the added vectors.
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition memory.h:63
Defines functions, macros and traits for allocating and using memory.
VectorAverage< float, 3 > VectorAverage3f
VectorAverage< float, 4 > VectorAverage4f
VectorAverage< float, 2 > VectorAverage2f
Defines all the PCL and non-PCL macros used.